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Getting Clarity on AI ROI: You’re Looking at It All Wrong

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The Point: For all the noise surrounding artificial intelligence (AI), most organizations still fail to capture meaningful return on investment (ROI). Leaders chase models, vendors, and pilots, yet the business case stays fuzzy and the outcomes underwhelm. The problem rarely lies with the technology. It lies with how organizations think about value, structure their operating models, and measure performance. AI ROI isn’t elusive. My experience: It’s simply being approached from the wrong direction!

Where AI ROI Breaks Down

94% of executives expect AI to create competitive advantage… Only 27% can point to meaningfully scaled outcomes. (Deloitte, State of AI in the Enterprise)

Across industries, the pattern is remarkably consistent. Organizations overestimate what AI can deliver in the short term, underestimate what it can deliver in the long term, and misdiagnose where value actually comes from.

A 2023 Deloitte global AI survey found that while 94 percent of executives expect AI to create competitive advantage, only 27 percent can point to meaningfully scaled outcomes (Deloitte, State of AI in the Enterprise). The gap is not conceptual; it’s operational. Most companies deploy AI as tools layered onto legacy processes rather than rethinking the processes themselves.

MIT Sloan’s research reinforces this. Companies that report the highest AI ROI invest 70 percent more in change management, workflow redesign, and capability building than their lower-performing peers (MIT Sloan Management Review, The Cultural Dividend of AI). In short: organizations expect returns from AI without transforming the environment needed to produce them.

Chasing the Wrong Metrics

Another root cause is measurement. Leaders track AI ROI as if they’re buying a server rack: cost in, efficiency out. That narrow frame kills strategic value before it ever emerges.

McKinsey’s 2024 Global AI Survey notes that top-performing AI organizations measure value across three horizons:

  1. Immediate efficiency gains
  2. Mid-term revenue acceleration
  3. Long-term business model transformation

Only 18 percent of companies track all three. Everyone else gets trapped in short-term savings, missing where the exponential value sits. AI’s highest ROI rarely comes from automating tasks. It comes from redefining how the enterprise creates value, from dynamic pricing to predictive operations to personalized product ecosystems.

BCG’s research found that companies using multi-horizon metrics are 2.5x more likely to report positive AI ROI (BCG, AI and the Future of Business Value). You can’t capture value you never measure.

You Don’t Have an AI ROI Problem. You Have an Operating Model Problem.

Technology is the easy part. Integrating it requires an operating model built for speed, experimentation, and cross-functional execution. Instead, most organizations treat AI as a feature bolted onto old habits.

Harvard Business Review’s analysis of failed AI programs points to three recurring issues (HBR, Why So Many AI Pilots Fail):

  • Fragmented ownership that leaves strategy unclear
  • Lack of data readiness, forcing teams to innovate on unstable foundations
  • Underinvestment in talent, training, and adoption

Executives want AI to behave like a neatly scoped IT project. But AI is an organizational capability, not a tool. It requires governance, decision rights, and incentives aligned with how algorithms learn and iterate. Without those, ROI will always look disappointing.

Sam Palazzolo Getting Clarity on ROI

The Organizations Getting AI ROI Right Do Five Things Differently

Across Deloitte, MIT, BCG, and McKinsey’s research, the pattern is strikingly consistent. High-return AI organizations:

1. Tie AI to fewer, higher-stakes business outcomes.

They don’t launch 30 pilots. They launch 3 that matter. They begin with revenue-critical, customer-facing, or operational choke points.

2. Invest in foundational data and infrastructure.

The companies realizing the strongest returns spend 40–60 percent of their AI investment on data readiness, not models (McKinsey). They optimize the soil before planting the seeds.

3. Rewire workflows, not just tasks.

AI ROI accelerates when companies redesign how decisions get made. Not “automate the process,” but “transform the process.”

4. Build skills and adoption into the plan from day one.

Top performers devote disproportionate energy to change management, training, and user enablement (MIT Sloan). AI only creates ROI if humans actually use it.

5. Measure value across efficiency, revenue, and transformation.

Companies that restrict AI ROI to cost savings stunt their upside. The biggest returns come when AI reshapes the business model.

If You Want AI ROI, Stop Treating AI as an Experiment

The uncomfortable truth for executives is this: AI isn’t underperforming. Your strategy is. Many organizations still treat AI as a sandbox initiative rather than a lever of enterprise value creation.

When leaders shift the conversation from “Which model should we buy?” to “Where is the bottleneck in our value chain that AI can fundamentally rewire?” everything changes. That’s when ROI stops being anecdotal and starts being systemic.

The next wave of competitive advantage won’t come from who deploys AI first. It will come from who deploys AI intelligently. Clarity on ROI requires clarity on strategy, operating model, measurement, and adoption. Get those right, and AI becomes not a cost center but a force multiplier.

Getting AI ROI isn’t hard.
Looking at it correctly is.

Sam Palazzolo, Real Strategies. Real Results.

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